Many dynamic, nonlinear systems exist which need adaptive forms of control. As an example, vibration and undesirable aeroelastic responses adversely affect various flexible structures (e.g., an aircraft wing). In turn, these adverse effects shorten the lifespans and increase the acquisition and maintenance costs of such structures. Thus, an active control system is desired for reducing vibration, alleviating buffet load and suppressing flutter of aircraft structures, providing adaptive hydraulic load control, reducing limit cycle oscillations of an aircraft store and the like.
U.S. Pat. Nos. 3,794,817, 4,358,822, 5,197,114 and 5,311,421, the entire disclosures of which are incorporated herein by reference, describe conventional controllers. In general, conventional adaptive control algorithms work almost entirely in the linear domain. Although U.S. Pat. No. 3,794,817 teaches a nonlinear adaptive controller, it requires that specific system knowledge about, for example, nonlinear deadband regions, be included for the controller to function.
Model-based predictive control systems, while sometimes adaptive, are generally linear and work with relatively large time constants (greater than one second). U.S. Pat. No. 4,358,822 discloses a typical adaptive predictive controller for use in a chemical process. In this instance, the controller is a linear adaptive model predictive controller with an eight minute time constant. Conventional controllers of this type generally use state space models for predicting future states.
Although some conventional controllers use neural networks as part of their control algorithm, such controllers typically include a separate controller in addition to the neural network. For example, U.S. Pat. No. 5,311,421 discloses such a process control method and system in which a neural network estimates certain parameters which are then used by a separate controller. Another use of neural networks in control systems is to learn control signal outputs from a conventional control algorithm or from a human operator as in U.S. Pat. No. 5,197,114.
Use of a neural network within a model predictive control scheme has been demonstrated but only for systems with relatively large time constants, such as controlling pH in a neutralization reactor.
For these reasons, a nonlinear adaptive controller which is not system specific and which learns nonlinearities in a neural network is desired. Further, such a controller is desired which has a relatively fast time constant of about one millisecond or faster and which does not need to copy the actions of another controller which must first be developed.